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I study deep learning models for Natural Language Processing (NLP). My research aims to make text understanding technology, e.g., machine translation, question answering and information extraction, widely accessible—to doctors, to teachers, to researchers or even to curious teenagers. To be broadly adopted, NLP technology needs to not only be accurate, but also reliable: models should provide explanations for their outputs; and the methods we use to evaluate them need to be convincing. My research addresses these two challenges: I develop theories that explain deep learning algorithms for NLP, and I seek ways to improve their evaluation.
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I am a postdoctoral researcher at the computer science and engineering department at The University of Washington, working with and at the Allen institute for Artificial intelligence. I completed my Ph.D. at the School of Computer Science and Engineering of The Hebrew University of Jerusalem, where I worked with Prof. Ari Rappoport.
In 2011, I graduated my masters degree (magna cum laude) in computer science, under the supervision of Prof. Ari Rappoport . Prior to that, I studied computer science and cognitive sciences at the Hebrew University, and completed my B.Sc. (magna cum laude) in 2008. I was a member of the Amirim program for outstanding undergraduate students. In 2004-2005, I was a software engineer at Check Point Ltd.
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Paul G. Allen Center for Computer Science & Engineering, Room 338
University of Washington
185 Stevens Way
Seattle, Washington, 98195-2350